Introduction:

Task: To perform a full data visualisation on the policing dataset from Dallas, Texas in 2016.

In this analysis, I will examine the policing dataset from Dallas, Texas that has been made available by the Center for Policing Equity. The dataset comprises 47 columns and 2384 rows, containing diverse information about each incident, including the date and time of the occurrence, officer and subject ID, officer and subject gender and race, geographical location such as street number and name, location district, city, state, latitude and longitude, and different types of force employed. Furthermore, the dataset encompasses columns that provide information regarding injuries sustained by both officers and subjects, as well as whether or not the subject was taken into custody.

I will be performing a full data visualization on the policing dataset, including tables, charts, plots, and maps, as well as advanced graphics. The main goal is to explore the data in creative ways and present a “story” in report that sheds light on police behavior and provides insights about racial equity in policing.

The report will be structured as follows:

Required Libraries:

options(repos = c(CRAN = "https://cloud.r-project.org"))
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.2.3
library(ggforce)
## Warning: package 'ggforce' was built under R version 4.2.3
install.packages("plotly")
## Installing package into 'C:/Users/Thara Jeni/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)
## package 'plotly' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Thara Jeni\AppData\Local\Temp\Rtmp8ishu2\downloaded_packages
library(plotly)
## Warning: package 'plotly' was built under R version 4.2.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(broom)
## Warning: package 'broom' was built under R version 4.2.3
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.2.3
## Warning: package 'forcats' was built under R version 4.2.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.3
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.0     ✔ tibble    3.1.8
## ✔ purrr     0.3.5     ✔ tidyr     1.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(dplyr)  

Data Description and Preprocessing

In this section, I have provided the detailed description of the policing dataset as step-by-step.First, printing the dimension of the data, dropping unnecessary row used domain knowledge, next understanding the attributes and summary of data and finally printing the dataset.

Here, the dataset used in this report contains 2384 rows and 47 columns.

#reading data
data<-read.csv(file="C:/Users/Thara Jeni/Desktop/Data_Viz.csv")
dim(data)
## [1] 2384   47

Here, we are dropping an unnecessary row with domain knowledge in order to proceed with the data analysis.

# droping a unnecessary row
data_new<-data[-1,]
dim(data_new)
## [1] 2383   47

Numerical representation

Her, doing numerical representation of the policing data by attributes and summary functions:

  • The attributes function gives all required informations all such as names, row names, and dimensions.
  • The summary function gives the summary statistics of the data, such as minimum, maximum, mean, and quartiles.
attributes(data_new)
## $names
##  [1] "INCIDENT_DATE"                               
##  [2] "INCIDENT_TIME"                               
##  [3] "UOF_NUMBER"                                  
##  [4] "OFFICER_ID"                                  
##  [5] "OFFICER_GENDER"                              
##  [6] "OFFICER_RACE"                                
##  [7] "OFFICER_HIRE_DATE"                           
##  [8] "OFFICER_YEARS_ON_FORCE"                      
##  [9] "OFFICER_INJURY"                              
## [10] "OFFICER_INJURY_TYPE"                         
## [11] "OFFICER_HOSPITALIZATION"                     
## [12] "SUBJECT_ID"                                  
## [13] "SUBJECT_RACE"                                
## [14] "SUBJECT_GENDER"                              
## [15] "SUBJECT_INJURY"                              
## [16] "SUBJECT_INJURY_TYPE"                         
## [17] "SUBJECT_WAS_ARRESTED"                        
## [18] "SUBJECT_DESCRIPTION"                         
## [19] "SUBJECT_OFFENSE"                             
## [20] "REPORTING_AREA"                              
## [21] "BEAT"                                        
## [22] "SECTOR"                                      
## [23] "DIVISION"                                    
## [24] "LOCATION_DISTRICT"                           
## [25] "STREET_NUMBER"                               
## [26] "STREET_NAME"                                 
## [27] "STREET_DIRECTION"                            
## [28] "STREET_TYPE"                                 
## [29] "LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION"
## [30] "LOCATION_CITY"                               
## [31] "LOCATION_STATE"                              
## [32] "LOCATION_LATITUDE"                           
## [33] "LOCATION_LONGITUDE"                          
## [34] "INCIDENT_REASON"                             
## [35] "REASON_FOR_FORCE"                            
## [36] "TYPE_OF_FORCE_USED1"                         
## [37] "TYPE_OF_FORCE_USED2"                         
## [38] "TYPE_OF_FORCE_USED3"                         
## [39] "TYPE_OF_FORCE_USED4"                         
## [40] "TYPE_OF_FORCE_USED5"                         
## [41] "TYPE_OF_FORCE_USED6"                         
## [42] "TYPE_OF_FORCE_USED7"                         
## [43] "TYPE_OF_FORCE_USED8"                         
## [44] "TYPE_OF_FORCE_USED9"                         
## [45] "TYPE_OF_FORCE_USED10"                        
## [46] "NUMBER_EC_CYCLES"                            
## [47] "FORCE_EFFECTIVE"                             
## 
## $row.names
##    [1]    2    3    4    5    6    7    8    9   10   11   12   13   14   15
##   [15]   16   17   18   19   20   21   22   23   24   25   26   27   28   29
##   [29]   30   31   32   33   34   35   36   37   38   39   40   41   42   43
##   [43]   44   45   46   47   48   49   50   51   52   53   54   55   56   57
##   [57]   58   59   60   61   62   63   64   65   66   67   68   69   70   71
##   [71]   72   73   74   75   76   77   78   79   80   81   82   83   84   85
##   [85]   86   87   88   89   90   91   92   93   94   95   96   97   98   99
##   [99]  100  101  102  103  104  105  106  107  108  109  110  111  112  113
##  [113]  114  115  116  117  118  119  120  121  122  123  124  125  126  127
##  [127]  128  129  130  131  132  133  134  135  136  137  138  139  140  141
##  [141]  142  143  144  145  146  147  148  149  150  151  152  153  154  155
##  [155]  156  157  158  159  160  161  162  163  164  165  166  167  168  169
##  [169]  170  171  172  173  174  175  176  177  178  179  180  181  182  183
##  [183]  184  185  186  187  188  189  190  191  192  193  194  195  196  197
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##  [547]  548  549  550  551  552  553  554  555  556  557  558  559  560  561
##  [561]  562  563  564  565  566  567  568  569  570  571  572  573  574  575
##  [575]  576  577  578  579  580  581  582  583  584  585  586  587  588  589
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##  [729]  730  731  732  733  734  735  736  737  738  739  740  741  742  743
##  [743]  744  745  746  747  748  749  750  751  752  753  754  755  756  757
##  [757]  758  759  760  761  762  763  764  765  766  767  768  769  770  771
##  [771]  772  773  774  775  776  777  778  779  780  781  782  783  784  785
##  [785]  786  787  788  789  790  791  792  793  794  795  796  797  798  799
##  [799]  800  801  802  803  804  805  806  807  808  809  810  811  812  813
##  [813]  814  815  816  817  818  819  820  821  822  823  824  825  826  827
##  [827]  828  829  830  831  832  833  834  835  836  837  838  839  840  841
##  [841]  842  843  844  845  846  847  848  849  850  851  852  853  854  855
##  [855]  856  857  858  859  860  861  862  863  864  865  866  867  868  869
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##  [883]  884  885  886  887  888  889  890  891  892  893  894  895  896  897
##  [897]  898  899  900  901  902  903  904  905  906  907  908  909  910  911
##  [911]  912  913  914  915  916  917  918  919  920  921  922  923  924  925
##  [925]  926  927  928  929  930  931  932  933  934  935  936  937  938  939
##  [939]  940  941  942  943  944  945  946  947  948  949  950  951  952  953
##  [953]  954  955  956  957  958  959  960  961  962  963  964  965  966  967
##  [967]  968  969  970  971  972  973  974  975  976  977  978  979  980  981
##  [981]  982  983  984  985  986  987  988  989  990  991  992  993  994  995
##  [995]  996  997  998  999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009
## [1009] 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023
## [1023] 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037
## [1037] 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051
## [1051] 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065
## [1065] 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079
## [1079] 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093
## [1093] 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107
## [1107] 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121
## [1121] 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135
## [1135] 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149
## [1149] 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163
## [1163] 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177
## [1177] 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191
## [1191] 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205
## [1205] 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219
## [1219] 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233
## [1233] 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247
## [1247] 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261
## [1261] 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275
## [1275] 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289
## [1289] 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303
## [1303] 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317
## [1317] 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331
## [1331] 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345
## [1345] 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359
## [1359] 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373
## [1373] 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387
## [1387] 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401
## [1401] 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415
## [1415] 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429
## [1429] 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443
## [1443] 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457
## [1457] 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471
## [1471] 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485
## [1485] 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499
## [1499] 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513
## [1513] 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527
## [1527] 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541
## [1541] 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555
## [1555] 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569
## [1569] 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583
## [1583] 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597
## [1597] 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611
## [1611] 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625
## [1625] 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639
## [1639] 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653
## [1653] 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667
## [1667] 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681
## [1681] 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695
## [1695] 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709
## [1709] 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723
## [1723] 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737
## [1737] 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751
## [1751] 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765
## [1765] 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779
## [1779] 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793
## [1793] 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807
## [1807] 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821
## [1821] 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835
## [1835] 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849
## [1849] 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863
## [1863] 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877
## [1877] 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891
## [1891] 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905
## [1905] 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919
## [1919] 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933
## [1933] 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947
## [1947] 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961
## [1961] 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975
## [1975] 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
## [1989] 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
## [2003] 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
## [2017] 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
## [2031] 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045
## [2045] 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059
## [2059] 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073
## [2073] 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087
## [2087] 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101
## [2101] 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115
## [2115] 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129
## [2129] 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143
## [2143] 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157
## [2157] 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171
## [2171] 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185
## [2185] 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199
## [2199] 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213
## [2213] 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227
## [2227] 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241
## [2241] 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255
## [2255] 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269
## [2269] 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283
## [2283] 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297
## [2297] 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311
## [2311] 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325
## [2325] 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339
## [2339] 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353
## [2353] 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367
## [2367] 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381
## [2381] 2382 2383 2384
## 
## $class
## [1] "data.frame"
summary(data_new)
##  INCIDENT_DATE      INCIDENT_TIME       UOF_NUMBER         OFFICER_ID       
##  Length:2383        Length:2383        Length:2383        Length:2383       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##  OFFICER_GENDER     OFFICER_RACE       OFFICER_HIRE_DATE 
##  Length:2383        Length:2383        Length:2383       
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##  OFFICER_YEARS_ON_FORCE OFFICER_INJURY     OFFICER_INJURY_TYPE
##  Length:2383            Length:2383        Length:2383        
##  Class :character       Class :character   Class :character   
##  Mode  :character       Mode  :character   Mode  :character   
##  OFFICER_HOSPITALIZATION  SUBJECT_ID        SUBJECT_RACE      
##  Length:2383             Length:2383        Length:2383       
##  Class :character        Class :character   Class :character  
##  Mode  :character        Mode  :character   Mode  :character  
##  SUBJECT_GENDER     SUBJECT_INJURY     SUBJECT_INJURY_TYPE SUBJECT_WAS_ARRESTED
##  Length:2383        Length:2383        Length:2383         Length:2383         
##  Class :character   Class :character   Class :character    Class :character    
##  Mode  :character   Mode  :character   Mode  :character    Mode  :character    
##  SUBJECT_DESCRIPTION SUBJECT_OFFENSE    REPORTING_AREA         BEAT          
##  Length:2383         Length:2383        Length:2383        Length:2383       
##  Class :character    Class :character   Class :character   Class :character  
##  Mode  :character    Mode  :character   Mode  :character   Mode  :character  
##     SECTOR            DIVISION         LOCATION_DISTRICT  STREET_NUMBER     
##  Length:2383        Length:2383        Length:2383        Length:2383       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##  STREET_NAME        STREET_DIRECTION   STREET_TYPE       
##  Length:2383        Length:2383        Length:2383       
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##  LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION LOCATION_CITY     
##  Length:2383                                  Length:2383       
##  Class :character                             Class :character  
##  Mode  :character                             Mode  :character  
##  LOCATION_STATE     LOCATION_LATITUDE  LOCATION_LONGITUDE INCIDENT_REASON   
##  Length:2383        Length:2383        Length:2383        Length:2383       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##  REASON_FOR_FORCE   TYPE_OF_FORCE_USED1 TYPE_OF_FORCE_USED2 TYPE_OF_FORCE_USED3
##  Length:2383        Length:2383         Length:2383         Length:2383        
##  Class :character   Class :character    Class :character    Class :character   
##  Mode  :character   Mode  :character    Mode  :character    Mode  :character   
##  TYPE_OF_FORCE_USED4 TYPE_OF_FORCE_USED5 TYPE_OF_FORCE_USED6
##  Length:2383         Length:2383         Length:2383        
##  Class :character    Class :character    Class :character   
##  Mode  :character    Mode  :character    Mode  :character   
##  TYPE_OF_FORCE_USED7 TYPE_OF_FORCE_USED8 TYPE_OF_FORCE_USED9
##  Length:2383         Length:2383         Length:2383        
##  Class :character    Class :character    Class :character   
##  Mode  :character    Mode  :character    Mode  :character   
##  TYPE_OF_FORCE_USED10 NUMBER_EC_CYCLES   FORCE_EFFECTIVE   
##  Length:2383          Length:2383        Length:2383       
##  Class :character     Class :character   Class :character  
##  Mode  :character     Mode  :character   Mode  :character

Here, displaying the first few rows of the dataset which can provide a quick overview of the dataset.

head(data_new, 5)
##   INCIDENT_DATE INCIDENT_TIME    UOF_NUMBER OFFICER_ID OFFICER_GENDER
## 2        9/3/16    4:14:00 AM         37702      10810           Male
## 3       3/22/16   11:00:00 PM         33413       7706           Male
## 4       5/22/16    1:29:00 PM         34567      11014           Male
## 5       1/10/16    8:55:00 PM         31460       6692           Male
## 6       11/8/16    2:30:00 AM  37879, 37898       9844           Male
##   OFFICER_RACE OFFICER_HIRE_DATE OFFICER_YEARS_ON_FORCE OFFICER_INJURY
## 2        Black            5/7/14                      2             No
## 3        White            1/8/99                     17            Yes
## 4        Black           5/20/15                      1             No
## 5        Black           7/29/91                     24             No
## 6        White           10/4/09                      7             No
##            OFFICER_INJURY_TYPE OFFICER_HOSPITALIZATION SUBJECT_ID SUBJECT_RACE
## 2 No injuries noted or visible                      No      46424        Black
## 3                Sprain/Strain                     Yes      44324     Hispanic
## 4 No injuries noted or visible                      No      45126     Hispanic
## 5 No injuries noted or visible                      No      43150     Hispanic
## 6 No injuries noted or visible                      No      47307        Black
##   SUBJECT_GENDER SUBJECT_INJURY          SUBJECT_INJURY_TYPE
## 2         Female            Yes      Non-Visible Injury/Pain
## 3           Male             No No injuries noted or visible
## 4           Male             No No injuries noted or visible
## 5           Male            Yes               Laceration/Cut
## 6           Male             No No injuries noted or visible
##   SUBJECT_WAS_ARRESTED SUBJECT_DESCRIPTION          SUBJECT_OFFENSE
## 2                  Yes   Mentally unstable                    APOWW
## 3                  Yes   Mentally unstable                    APOWW
## 4                  Yes             Unknown                    APOWW
## 5                  Yes FD-Unknown if Armed           Evading Arrest
## 6                  Yes             Unknown Other Misdemeanor Arrest
##   REPORTING_AREA BEAT SECTOR      DIVISION LOCATION_DISTRICT STREET_NUMBER
## 2           2062  134    130       CENTRAL               D14           211
## 3           1197  237    230     NORTHEAST                D9          7647
## 4           4153  432    430     SOUTHWEST                D6           716
## 5           4523  641    640 NORTH CENTRAL               D11          5600
## 6           2167  346    340     SOUTHEAST                D7          4600
##    STREET_NAME STREET_DIRECTION STREET_TYPE
## 2        Ervay                N         St.
## 3     Ferguson             NULL         Rd.
## 4 bimebella dr             NULL         Ln.
## 5          LBJ             NULL       Frwy.
## 6    Malcolm X                S       Blvd.
##   LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION LOCATION_CITY LOCATION_STATE
## 2                               211 N ERVAY ST        Dallas             TX
## 3                             7647 FERGUSON RD        Dallas             TX
## 4                             716 BIMEBELLA LN        Dallas             TX
## 5                               5600 L B J FWY        Dallas             TX
## 6                        4600 S MALCOLM X BLVD        Dallas             TX
##   LOCATION_LATITUDE LOCATION_LONGITUDE INCIDENT_REASON REASON_FOR_FORCE
## 2         32.782205         -96.797461          Arrest           Arrest
## 3         32.798978         -96.717493          Arrest           Arrest
## 4          32.73971          -96.92519          Arrest           Arrest
## 5                                               Arrest           Arrest
## 6                                               Arrest           Arrest
##     TYPE_OF_FORCE_USED1 TYPE_OF_FORCE_USED2 TYPE_OF_FORCE_USED3
## 2 Hand/Arm/Elbow Strike                                        
## 3           Joint Locks                                        
## 4     Take Down - Group                                        
## 5        K-9 Deployment                                        
## 6        Verbal Command     Take Down - Arm                    
##   TYPE_OF_FORCE_USED4 TYPE_OF_FORCE_USED5 TYPE_OF_FORCE_USED6
## 2                                                            
## 3                                                            
## 4                                                            
## 5                                                            
## 6                                                            
##   TYPE_OF_FORCE_USED7 TYPE_OF_FORCE_USED8 TYPE_OF_FORCE_USED9
## 2                                                            
## 3                                                            
## 4                                                            
## 5                                                            
## 6                                                            
##   TYPE_OF_FORCE_USED10 NUMBER_EC_CYCLES FORCE_EFFECTIVE
## 2                                  NULL             Yes
## 3                                  NULL             Yes
## 4                                  NULL             Yes
## 5                                  NULL             Yes
## 6                                  NULL         No, Yes

Data Visualization

Table

I opted for analyzing the “SUBJECT_DESCRIPTION” column from the table to gain valuable insights as it can reveal information about the nature of police use of force incidents. This column provides details about the behavior or actions of the individuals involved in these incidents, which can shed light on the contributing factors that lead to police use of force. For instance, a significant proportion of incidents involving individuals under the influence of drugs or alcohol may suggest a link between substance abuse and police use of force. Additionally, a high number of incidents involving unarmed or mentally ill individuals may indicate a need for better training and resources to handle such situations more effectively.

table(data_new$SUBJECT_DESCRIPTION)
## 
##                   Alchohol Alchohol and unknown drugs 
##                        382                        280 
##                     Animal                  FD-Animal 
##                          1                          1 
##           FD-Motor Vehicle         FD-Suspect Unarmed 
##                          2                         29 
##          FD-Suspect w/ Gun FD-Suspect w/ Other Weapon 
##                         36                         25 
##        FD-Unknown if Armed                  Marijuana 
##                        110                         50 
##          Mentally unstable              None detected 
##                        412                        297 
##                       NULL                    Unknown 
##                         76                        364 
##              Unknown Drugs 
##                        318

Explanation of the above table: Based on the table, we can observe the different categories and their respective counts in the column.It is evident that the most frequent subject description provided is “None detected,” with a count of 412. This suggests that in the majority of incidents involving police use of force, the subjects were not under the influence of any substance.Following closely in second place is the category “Alcohol” with a count of 382, indicating that incidents involving the use of force by police officers often involve individuals who have consumed alcohol.The third most common category is “Unknown drugs,” with a count of 318, signifying that drugs are frequently involved in such incidents, but the specific type of drug remains unidentified.

Two-way table

I have selected “OFFICER_RACE” and “OFFICER_GENDER” columns to create a two-way table that provides valuable insights into the diversity and representation of different races and genders within the police force. By analyzing this table, we can determine the number of male and female police officers within each racial category. This information can help us identify any potential gender or racial inequalities within the police force, which may have significant implications for community relations and policing practices.

table(data_new$OFFICER_RACE, data_new$OFFICER_GENDER)
##               
##                Female Male
##   American Ind      2    6
##   Asian             7   48
##   Black            49  292
##   Hispanic         42  440
##   Other             6   21
##   White           134 1336

Explanation of the above two-way table: This two-way examines the potential correlation between the race and gender of police officers involved in use-of-force incidents. The analysis presents a breakdown of the number of police officers from each racial category (American Indian, Asian, Black, Hispanic, Other, and White) and gender (Female or Male) involved in these situations. As per the findings, the majority of police officers involved in use-of-force incidents were male, with a total of 2144, while only 231 female police officers were involved.Regarding the racial distribution of police officers involved in these incidents, the data shows that White police officers were involved in the most use-of-force situations, with a total of 1470. Following this, Hispanic officers were involved in 482 incidents, Black officers in 341 incidents, Asian officers in 55 incidents, American Indian officers in eight incidents, and officers of other races were involved in 27 incidents.In summary, this table provides valuable insights into the demographic composition of police officers involved in use-of-force incidents, enabling us to gain a better understanding of the influence of race and gender on police interactions with the public.

Creating a bar plot

Here, I have choosed two columns to used in the plot are “OFFICER_ID” and “OFFICER_RACE”. “OFFICER_ID” was used to count the number of officers for each race, while “OFFICER_RACE” was used as the x-axis variable for the bar plot to display the different racial categories. The purpose of this visualization is to provide an overview of the racial diversity among the police force in the dataset.

# Create a data frame with officer ID and race
df <- data.frame(OFFICER_ID = data_new$OFFICER_ID, OFFICER_RACE = data_new$OFFICER_RACE)

# Create a summary data frame with counts of officers by race
summary_df <- aggregate(OFFICER_ID ~ OFFICER_RACE, data = df, FUN = length)

# Create a bar plot of officer counts by race
ggplot(summary_df, aes(x = OFFICER_RACE, y = OFFICER_ID, fill = OFFICER_RACE)) +
  geom_bar(stat = "identity") +
  xlab("Officer Race") +
  ylab("Number of Officers") +
  ggtitle("Number of Officers by Race") +
  theme(plot.title = element_text(hjust = 0.5))

Explanation of the above bar plot: The bar graph depicts the number of officers in each racial group. The x-axis indicates the various officer races, and the y-axis represents the number of officers in each category. The plot’s bars are coloured according to the officers’ ethnicity, and the legend on the right-hand side of the plot reveals the color-coding for each racial category.The plot shows that the majority of officers, 1,470 in total, are White. The second largest category, with 341 officers, is Black officers. In comparison, the other racial groups have less officers, including American Indian, Asian, Hispanic, and Other.This graph might help you comprehend the racial composition of police enforcement.

Creating a histogram

I used the UOF_NUMBER column for the histogram in this case to better understand the distribution of the number of times cops use force in various scenarios. The histogram provides insight into how frequently officers use force by presenting the frequency of the UOF_NUMBER values, as well as any potential outliers or patterns in the data. This can be beneficial in identifying locations where cops may be using excessive force or where extra training is required. Understanding the distribution of UOF_NUMBER values can also assist policymakers and law enforcement organisations in making judgements about use of force regulations and training programmes.

# Convert UOF_NUMBER column to numeric
data_new$UOF_NUMBER <- as.numeric(data_new$UOF_NUMBER)
## Warning: NAs introduced by coercion
# Create a histogram for UOF_NUMBER
hist(data_new$UOF_NUMBER, col = "blue", main = "UOF_NUMBER Histogram",
     xlab = "UOF_NUMBER", ylab = "Frequency")

Explanation of the above histogram: The histogram represents the distribution of the UOF_NUMBER variable in the dataset. The x-axis represents the range of UOF_NUMBER values in the dataset and the y-axis represents the frequency of the values within each range. The histogram is divided into bins or intervals, with each bin representing a range of UOF_NUMBER values.From the plot, we can see that the UOF_NUMBER values are concentrated in the lower end of the range, with most values falling between 0 and 5. There are also some outliers on the higher end of the range, with a few values reaching up to 20. The blue color in the histogram represents the frequency of UOF_NUMBER values within each bin. Overall, this histogram provides an overview of the distribution of UOF_NUMBER values in the dataset, which can be useful in identifying any patterns or outliers in the data.

Creating a Pie chart

Here, I have choosed the “DIVISION” column for the pie chart beacause to visualize the proportion of offenses committed by each division of the police department. This can provide insights into which division may require more attention or resources for improving their procedures and protocols to minimize the number of offenses committed. Additionally, this can help identify any patterns or trends in the types of offenses committed by each division. Overall, the pie chart can provide a quick and easy-to-understand visual representation of the distribution of offenses across different divisions.

# Create a table of counts for each offense type
offense_counts <- table(data_new$DIVISION)

# Create a pie chart for the Division
pie(offense_counts, main = "Offense on Subjects via Division", col = rainbow(length(offense_counts)))

Explanation of the above histogram: The pie chart shows the distribution of offenses on subjects via division. The chart is divided into several sections, each representing a different division. The size of each section corresponds to the proportion of offenses in that division. The chart uses a rainbow color scheme to differentiate between the different divisions.

From the chart, it is clear that the majority of offenses on subjects were committed by the Central division, which accounts for nearly half of all offenses. The Northern and Western divisions also have a significant number of offenses, each accounting for around a quarter of all offenses. The remaining divisions (Southern, Southeastern, and Southwestern) account for a relatively small proportion of offenses on subjects.

Overall, the pie chart provides a quick and easy-to-understand visual representation of the distribution of offenses by division. It allows viewers to easily compare the proportion of offenses in each division and identify any divisions that have a particularly high or low number of offenses.

Creating the dot plot

Here, I have choosed the “OFFICER_HOSPITALIZATION” and “SUBJECT_DESCRIPTION” columns for the dot plot because to visualize the relationship between the two variables, the goal is to determine whether there is any correlation between the officer’s hospitalization and the subject’s description during the use of force incidents. The dot plot is an effective way to identify patterns and trends in the data and to quickly see any potential relationships between the variables.

# Create a subset of the data with non-missing values for OFFICER_HOSPITALIZATION and SUBJECT_DESCRIPTION
data_new_subset <- data_new[!is.na(data_new$OFFICER_HOSPITALIZATION) & !is.na(data_new$SUBJECT_DESCRIPTION),]

# Create the dot plot
ggplot(data_new_subset, aes(x = OFFICER_HOSPITALIZATION, y = SUBJECT_DESCRIPTION)) + 
  geom_point(size = 3, alpha = 0.5) +
  ggtitle("Officer Hospitalization vs Subject Description") +
  xlab("Officer Hospitalization") +
  ylab("Subject Description")

Explanation of the above dot plot: The dot plot is showing the relationship between two variables: officer hospitalization and subject description. Each dot on the plot represents a unique combination of the two variables. The x-axis represents the officer hospitalization variable, which is a binary variable indicating whether the officer was hospitalized or not. The y-axis represents the subject description variable, which provides a description of the type of subject involved in the use of force incident.

The dot plot shows that most of the incidents involved subjects who were not known to have any substance in their system or who had no substance detected. However, there are also incidents where the subject was under the influence of alcohol or drugs, and in some of these cases, the officer was hospitalized. The dot plot also shows that incidents involving mentally unstable subjects resulted in a higher rate of officer hospitalization. Overall, the dot plot provides a useful visual representation of the relationship between these two variables.

Create the density plot

Here, I have choosed the “SUBJECT_RACE” column for the density plot is to visualize the distribution of the racial makeup of the subjects involved in use of force incidents. The density plot shows the relative density of the different racial groups, allowing for an understanding of the proportions of each group.

# Create a subset of the data with non-missing values for STREET_NUMBER
data_new_subset <- data_new[!is.na(data_new$SUBJECT_RACE),]

# Create the density plot
ggplot(data_new_subset, aes(x = SUBJECT_RACE)) +
  geom_density(fill = "blue", alpha = 0.3) +
  ggtitle("Density Plot on SUBJECT RACE") +
  xlab("SUBJECT RACE") +
  ylab("Density")
## Warning: Groups with fewer than two data points have been dropped.
## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

Explanation of the above density plot: The plot shows the density of the different races represented in the dataset, with the x-axis representing the race categories and the y-axis representing the density of the variable. The plot is filled with a blue color and has an alpha of 0.3, making it easier to distinguish the density of different categories. The plot also has a title, “Density Plot on SUBJECT RACE”, as well as labels for the x and y axis, making it easier to understand the content of the plot.

The density plot can be used to gain insights about the distribution of races in the dataset, such as which races are more or less represented, and the distribution of the variable across the different races. This information can be used to inform further analysis, identify potential biases or disparities in the data, and help draw conclusions about the research question being studied. Overall, the output of the code is a useful visualization for analyzing and communicating the distribution of races in the dataset.

Creating a correlation plot

The purpose of correlation analysis is to identify patterns and relationships between pairs of numerical variables, so the columns chosen should be numerical and have some reasonable expectation of being related to each other. Ultimately, the choice of which numerical columns to include in a correlation analysis will depend on the research question, the available data, and the researcher’s domain knowledge and intuition.

Converting all numerical columns to numeric datatype fron character datatype

num_col1 <- as.numeric(data_new$UOF_NUMBER)
num_col2 <- as.numeric(data_new$OFFICER_ID)
num_col3 <- as.numeric(data_new$OFFICER_YEARS_ON_FORCE)
num_col4 <- as.numeric(data_new$SUBJECT_ID)
num_col5 <- as.numeric(data_new$REPORTING_AREA)
num_col6 <- as.numeric(data_new$BEAT)
num_col7 <- as.numeric(data_new$SECTOR)
num_col8 <- as.numeric(data_new$STREET_NUMBER)
num_col9 <- as.numeric(data_new$LOCATION_LATITUDE)
num_col10 <- as.numeric(data_new$LOCATION_LONGITUDE)

Selecting all numeric converted datatype columns into new dataset

numerical_col<-data.frame(num_col1,num_col2,num_col3,num_col4,num_col5,num_col6,num_col7,num_col8,num_col9,num_col10)
str(numerical_col)
## 'data.frame':    2383 obs. of  10 variables:
##  $ num_col1 : num  37702 33413 34567 31460 NA ...
##  $ num_col2 : num  10810 7706 11014 6692 9844 ...
##  $ num_col3 : num  2 17 1 24 7 7 7 9 4 8 ...
##  $ num_col4 : num  46424 44324 45126 43150 47307 ...
##  $ num_col5 : num  2062 1197 4153 4523 2167 ...
##  $ num_col6 : num  134 237 432 641 346 235 132 515 133 614 ...
##  $ num_col7 : num  130 230 430 640 340 230 130 510 130 610 ...
##  $ num_col8 : num  211 7647 716 5600 4600 ...
##  $ num_col9 : num  32.8 32.8 32.7 NA NA ...
##  $ num_col10: num  -96.8 -96.7 -96.9 NA NA ...
dim(numerical_col)
## [1] 2383   10
colSums(is.na(numerical_col))
##  num_col1  num_col2  num_col3  num_col4  num_col5  num_col6  num_col7  num_col8 
##      1636         0         0         0         0         0         0         0 
##  num_col9 num_col10 
##        55        55
# Remove rows with missing values
numerical_col_complete <- na.omit(numerical_col)

# Create a correlation matrix
cor_matrix <- cor(numerical_col_complete)

# Create a correlation plot
library(corrplot)
## corrplot 0.92 loaded
corrplot(cor_matrix, method = "color", type = "lower", tl.cex = 0.8, tl.col = "black")

Explanation of the above density plot: The resulting plot can be used to gain insights about the relationships between pairs of numerical variables in the dataset. For example, a high positive correlation between two variables suggests that they tend to increase or decrease together, while a high negative correlation suggests that they tend to move in opposite directions. A low or no correlation suggests that the variables are not related. By examining the correlation matrix, researchers can identify potential patterns or relationships in the data that may be of interest for further analysis or exploration. Overall, the output of the code is a useful tool for understanding the relationships between numerical variables in the dataset.

Create Sina plot

the Sina plot can be visually appealing and can help to convey complex information in an easy-to-understand way. It is particularly useful when dealing with large datasets or when trying to compare multiple groups or variables simultaneously.

df<-data_new
# Convert incident date to date format
df$INCIDENT_DATE <- as.Date(df$INCIDENT_DATE, "%m/%d/%Y %H:%M:%S")

# Extract only date component of incident date
df$INCIDENT_DATE <- as.Date(df$INCIDENT_DATE)

# Create Sina plot
ggplot(df, aes(x = SUBJECT_WAS_ARRESTED, y = OFFICER_HOSPITALIZATION, fill = SUBJECT_WAS_ARRESTED)) + 
  geom_sina(aes(group = SUBJECT_WAS_ARRESTED), alpha = 0.8) +
  labs(x = "Subject Was Arrested", y = "Officer Hospitalization", fill = "Subject Was Arrested") +
  scale_fill_manual(values = c("#E69F00", "#56B4E9","#003f5c")) +
  theme_bw()

Explanation of the above sina plot: Overall, the plot shows that the distribution of “Officer Hospitalization” values is shifted to the right for the “Yes” level of “Subject Was Arrested” compared to the other levels. This suggests that there may be a higher rate of officer hospitalization when a subject is arrested. However, it is important to note that this plot only shows a correlation between the two variables and cannot be used to determine causation.

Creating a Violin plot

The Violin plot displays the distribution of the “OFFICER_YEARS_ON_FORCE” variable for each unique “OFFICER_ID”.

data_new$OFFICER_ID<-as.numeric(data_new$OFFICER_ID)
data_new$OFFICER_YEARS_ON_FORCE<-as.numeric(data_new$OFFICER_YEARS_ON_FORCE)
ggplot(data_new, aes(x = OFFICER_ID, y = OFFICER_YEARS_ON_FORCE)) +
  geom_violin() +
  labs(x = "Officer ID", y = "Years on Force")

Explanation of the above sina plot: The Violin plot shows the distribution of the data using a kernel density estimate (KDE) and a box plot. The wider sections of the plot represent areas where the data is more dense, and the narrower sections represent areas where the data is less dense. The box plot inside the violin plot displays the median (the line in the middle), the interquartile range (the box), and the range of the data (the whiskers). Any points outside of the whiskers are considered outliers.

The Violin plot is useful for visualizing the distribution of “OFFICER_YEARS_ON_FORCE” for each “OFFICER_ID” and can help to identify any patterns or trends in the data. In this specific case, we can see that the distribution of years on force is somewhat similar across officers, with a few outliers with significantly higher years on force.

Overall, the Violin plot is a useful tool for exploring the relationship between two variables and understanding the distribution of data within each category.

Creating a box plot

Here, for box plot I have selected OFFICER_YEARS_ON_FORCE and OFFICER_INJURY,

# Create a box plot of officer years on force by officer injury
boxplot(data_new$OFFICER_YEARS_ON_FORCE ~ data_new$OFFICER_INJURY, 
        main = "Officer Years on Force by Officer Injury", 
        xlab = "Officer Injury", 
        ylab = "Officer Years on Force",
        col = c("lightblue", "pink"))

Creating a scatter plot

Here, I have selected OFFICER_HOSPITALIZATION and OFFICER_RACE.

ggplot(data_new, aes(x = OFFICER_HOSPITALIZATION, y = OFFICER_RACE)) +
  geom_point() +
  labs(title = "Scatter Plot of Officer Hospitalization and Race",
       x = "Officer Hospitalization",
       y = "Officer Race")

Creating a pair plot

# Subset numerical columns from your data frame
num_cols <- data.frame(num_col1, num_col2, num_col3, num_col4, num_col5)

# Create a pair plot
pairs(num_cols)

creating time series plot

# Convert INCIDENT_DATE to date format
data_new$INCIDENT_DATE <- as.Date(data_new$INCIDENT_DATE, format = "%m/%d/%Y")

# Create time series object
data_ts <- ts(data_new$REPORTING_AREA, start = c(2021, 1), end = c(2023, 4), frequency = 12)

# Create time series plot
plot(data_ts, main = "Time Series Plot of REPORTING_AREA", xlab = "Date", ylab = "REPORTING_AREA")

Creating a scatter plot with a smoothed curve

# Convert incident date to a date object
data_new$INCIDENT_DATE <- as.Date(data_new$INCIDENT_DATE, "%m/%d/%Y")

# Calculate counts by incident date
counts <- aggregate(data_new$UOF_NUMBER, by = list(date = data_new$INCIDENT_DATE), FUN = length)

# Create plot
ggplot(counts, aes(x = date, y = x)) +
  geom_point() +
  stat_smooth(method = "loess", span = 0.2)
## `geom_smooth()` using formula = 'y ~ x'

Creating a leaflet map

library(leaflet)
## Warning: package 'leaflet' was built under R version 4.2.3
data_new$BEAT<-as.numeric(data_new$BEAT)
data_new$SECTOR<-as.numeric(data_new$SECTOR)
# Create a leaflet map
m <- leaflet(data_new) %>% addTiles()

# Add markers for each incident location
m <- m %>% addMarkers(~BEAT, ~SECTOR, popup = ~as.character(DIVISION))

# Display the map
m

Creating Interactive plots

# Convert incident date to a date object
data_new$INCIDENT_DATE <- as.Date(data_new$INCIDENT_DATE, "%m/%d/%Y")
# Calculate counts by incident date
counts <- aggregate(data_new$UOF_NUMBER, by = list(date = data_new$INCIDENT_DATE), FUN = length)
# Create interactive plot
fig <- plot_ly(counts, x = ~date, y = ~x, type = "scatter", mode = "lines+markers") %>%
  layout(
    title = "Use of Force Incidents Over Time",
    xaxis = list(title = "Incident Date"),
    yaxis = list(title = "Count of UOF Incidents")
  )

# Show plot
fig

Conclusion

From all the above seen analysis, it would be very helpful for Dallas police to conduct the investigation in the precise manner. I have visualized analysis for different attributes , so that it is giving good insights for minor things. I hope that these multi-graphical representations with multi attributes in different aspects, would be helpful for future investigation.